text string |
|---|
<filename>examples/mnist_sl/extract_mnist.py
#!/usr/bin/env python
# coding: utf-8
"""
File Name: extract_mnist.py
Author: <NAME>
E-mail: <EMAIL>
Created on: Tue Oct 13 21:13:51 2015 CST
"""
DESCRIPTION = """
"""
import os
import argparse
import logging
from struct import unpack
import numpy as np
f... |
<filename>fitting/LimitCalculator_MC.py
"""
LimitCalculator_MC.py - 23/03/2017
Summary:
Tool for calculating limits on New Physics models
from measurements of Coherent Elastic Neutrino Nucleus
Scattering (CEvNS). Uses MCMC to sample the likelihood.
Requires numpy, scipy and CEvNS.py.
Also requires emcee - http://da... |
<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Sat Dec 24 15:28:10 2016
@author: User
"""
import random, math
import scipy.io
import matplotlib as mpl
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import time
import matplotlib.pyplot as plt
from sklearn import svm
from sklearn.svm imp... |
<gh_stars>1-10
# Copyright 2020 Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ... |
<reponame>twiewiora/smell-simulation
import glob
import os
from statistics import median
from itertools import groupby, product
from matplotlib import pyplot as plt
# general settings
PREFIXES = ['formin', 'fortwist', 'torch', 'smog']
VARIANTS = ['default', 'variant1', 'variant2', 'variant3']
WORKERS_ROOT_MAX = 4
SAM... |
<filename>core_compute.py<gh_stars>0
import sys
import time
import kombine
import os
import numpy as np
import pandas as pd
import scipy.stats as ss
from ptemcee import Sampler as PTSampler
from multiprocessing import Pool
#from pymultinest.solve import solve
def coef_summary(flattrace, pname, outname):
headings... |
from astropy.io import fits
from sitelle.utils import *
import numpy as np
from scipy.interpolate import UnivariateSpline
from orb.utils import io
import subprocess
import os
import copy
import sys
from path import Path
import socket
__all__ = ['parameter_map', 'read', 'extract_spectrum', 'sew_spectra', 'NburstFitter'... |
<gh_stars>0
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 27 09:57:52 2020
Split the data into training, validation and test sets
@author: <NAME>, <NAME>
"""
from sklearn.model_selection import StratifiedShuffleSplit
import scipy.io as sio
import numpy as np
from preprocess import preprocess
import pandas as pd
impo... |
<reponame>dc-aichara/signate-jpx
import yaml
import pandas as pd
import numpy as np
from scipy.stats import spearmanr
from PriceIndices import Indices
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, MinMaxScaler
from typing import Tuple, Optional, Union
import lightgbm as lgb
def load_data(
data_... |
import numpy as np
import matplotlib.pyplot as plt
from scipy.io import wavfile
# Read the input file
sampling_freq, audio = wavfile.read('input_read.wav')
# Print the params
print '\nShape:', audio.shape
print 'Datatype:', audio.dtype
print 'Duration:', round(audio.shape[0] / float(sampling_freq), 3), 'seconds'
# N... |
<reponame>igotchalk/simpegEM1D
import scipy as sp
import numpy as np
from SimPEG.regularization import Sparse, SparseSmall, SparseDeriv, Simple
from SimPEG import Mesh, Utils
def get_2d_mesh(n_sounding, hz):
"""
Generate 2D mesh for regularization
hx:
hz:
"""
hx = np.ones(n_sound... |
<filename>rcc_dp/sqkr_test.py
# Copyright 2021, Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicab... |
<filename>fish/scripts/save_dff.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# Register, downsample, save dff as tif
#
# <NAME>
# <EMAIL>
#
# License: MIT
#
def get_sc(app_name):
from pyspark import SparkConf, SparkContext
conf = SparkConf().setAppName(app_name)
sc = SparkContext(conf=conf)
r... |
<filename>hardware/channel.py
from collections import deque
import math
import numpy as np
from scipy import signal
class Channel:
def __init__(self, name, min, max, maxNum, offset=0.0):
self.name = name
self.min = min
self.max = max
self.num = 0
self.sum = 0
self.buffersum = 0
self.size = maxNum
se... |
"""
Script calculates accuracy of multi-decadal ANNv1
Author : <NAME>
Date : 19 January 2021
"""
### Import modules
import numpy as np
import scipy.stats as sts
import matplotlib.pyplot as plt
import calc_Utilities as UT
import calc_dataFunctions as df
import palettable.wesanderson as ww
import calc_Stats as ... |
<reponame>Code-Cornelius/python_libraries<filename>corai_util/finance/src/implied_vol.py
# normal libraries
import warnings
import numpy as np
from scipy.optimize import bisect
from scipy.stats import norm
# priv_libraries
from corai_util.finance.src.bs_model import BlackScholes, BlackScholesVegaCore
from corai_util.... |
# -*- coding: utf-8 -*-
"""
Created on Tues at some point in time
@author: bokorn with some code pulled from https://github.com/yuxng/PoseCNN/blob/master/lib/datasets/lov.py
"""
import os
import cv2
import torch
import numpy as np
import scipy.io as sio
import time
import sys
from se3_distributions.datasets.image_dat... |
import argparse
import numpy as NP
from astropy.io import fits
from astropy.io import ascii
import scipy.constants as FCNST
import matplotlib.pyplot as PLT
import matplotlib.colors as PLTC
import progressbar as PGB
import healpy as HP
import geometry as GEOM
import interferometry as RI
import catalog as SM
import cons... |
<filename>dml/KNN/kd.py
from __future__ import division
import numpy as np
import scipy as sp
from operator import itemgetter
from scipy.spatial.distance import euclidean
from dml.tool import Heap
class KDNode:
def __init__(self,x,y,l):
self.x=x
self.y=y
self.l=l
self.F=None
self.Lc=None
self.Rc=None
sel... |
"""
Created on Thu Jan 26 17:04:11 2017
Preprocess Luna datasets and create nodule masks (and/or blank subsets)
NOTE that:
1. we do NOT segment the lungs at all -- we will use the raw images for training (DO_NOT_SEGMENT = True)
2. No corrections are made to the nodule radius in relation to the thickness of th... |
<filename>tests/test_choice_calcs.py<gh_stars>1-10
"""
Tests for the choice_calcs.py file.
"""
import unittest
import warnings
from collections import OrderedDict
import numpy as np
import numpy.testing as npt
import pandas as pd
from scipy.sparse import csr_matrix
from scipy.sparse import diags
from scipy.sparse impo... |
<gh_stars>0
# This file is part of GridCal.
#
# GridCal is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# GridCal is distributed in t... |
<filename>DataPipeline/DataManagerFinal.py
import os
import csv
import re
import csv
import math
from collections import defaultdict
from scipy.signal import butter, lfilter
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
from statistics import mean
from scipy.stats import kurtosis, skew
from skl... |
"""Graph module to store a network and generate random walks from it."""
import numpy as np
from scipy import sparse
from residual2vec import utils
class NodeSampler:
def fit(self, A):
"""Fit the sampler.
:param A: adjacency matrix
:type A: scipy.csr_matrix
:raises NotImplemented... |
#!/usr/bin/env python
# coding: utf-8
from sympy import Symbol
from sympy import pprint
def comment():
with open("180401054_yorum.txt", "w") as comment:
comment.write("CEYDA KAMALI 180401054\n")
comment.write("İntegral hesaplama işlemlerini yaparken yamuk metodunu kullandım.\n")
commen... |
<filename>qclib/isometry.py
# Copyright 2021 qclib project.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable l... |
<reponame>EFrion/montepython_public
from scipy import interpolate
import os
import numpy as np
import montepython.io_mp as io_mp
from montepython.likelihood_class import Likelihood
class bbn_omegab(Likelihood):
# initialization routine
def __init__(self, path, data, command_line):
Likelihood.__init_... |
__author__ = 'thor'
import matplotlib.pyplot as plt
import numpy as np
import scipy
def xy_density(xdat, ydat, cmap='jet', marker='.', imshow_kwargs={},
bins=[100, 100], density_thresh=0, xyrange=None, plot_kwargs={}):
'''
graphs the density of (x,y) points in the plane, using color (defined b... |
import numpy as np
import numpy.random as npr
import scipy.stats as ss
import utilities as ut
class Model:
"""
The models (objects of this class) represent quantum circuits that
operate on na + nb qubits.
We will call a list1 any list of numpy arrays of shapes given by the
shapes1, with length gi... |
import numpy as np
import torch
import torch.nn as nn
from scipy.sparse import issparse
from fonduer.learning.disc_learning import NoiseAwareModel
from fonduer.learning.disc_models.layers.rnn import RNN
from fonduer.learning.disc_models.utils import (
SymbolTable,
mark_sentence,
mention_to_tokens,
pad_... |
from subprocess import call
import os, time
import shutil
import io
import base64
from IPython.display import HTML
import numpy as np
from PIL import ImageDraw, Image, ImageFont
from tempfile import NamedTemporaryFile
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import animati... |
<reponame>EmPlatts/FRB
""" Module for IGM calculations
"""
from __future__ import print_function, absolute_import, division, unicode_literals
import numpy as np
import os
from IPython import embed
from pkg_resources import resource_filename
from scipy.interpolate import interp1d
from scipy.interpolate import Interpo... |
import numpy as np
import math
import scipy.stats
def main():
N = 100000
num_iter = 200
k = 13
l = 3
alpha = 1.0*l/(k-l)
print(alpha)
eps = 0.005
for cov in [2.20,2.22,2.24,2.26]:
print(cov)
lamb = cov/(1+alpha)
P_b = sampled_DE(lamb, num_iter, N, eps, k, l)
print(P_b)
def sampled_DE(lamb, num_iter, ... |
"""Performs face alignment and calculates L2 distance between the embeddings of images."""
# MIT License
#
# Copyright (c) 2016 <NAME>
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software wit... |
<reponame>SanGreel/music-recommendation-system
#!/usr/bin/env python
# coding: utf-8
get_ipython().run_line_magic('pylab', 'inline')
import warnings
warnings.filterwarnings('ignore')
import numpy as np
import matplotlib.pyplot as plt
from .audiofile_read import *
from .rp_extract import rp_extract
#from rp_plot import... |
from MLModule.metric import np_celoss_pair
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from scipy.special import entr
sns.set_style("white")
sns.set(font_scale=2)
kwargs = dict(hist_kws={'alpha': .4}, kde_kws={'linewidth': 2, "bw_adjust": 0.3})
df_by_ele = pd.... |
# AUTOGENERATED! DO NOT EDIT! File to edit: nbs/datasets/datasets.beibei.ipynb (unless otherwise specified).
__all__ = ['BeibeiDataset']
# Cell
import numpy as np
import scipy.sparse as sp
import pickle
from .bases.common import Dataset
from ..utils.common_utils import *
# Cell
class BeibeiDataset(Dataset):
def... |
from abc import ABC, abstractmethod
from typing import MutableSequence
import numpy as np
from numpy.core.fromnumeric import size, transpose
from scipy.signal.ltisys import LinearTimeInvariant
from .model import Model
from scipy import signal
from si.util.metrics import mse, mse_prime
from si.util.im2col import pad2... |
<filename>src/preprocess/archive/preprocess_mongo.py
import json
import numpy as np
import cPickle as pickle
import progressbar
from pymongo import MongoClient
from scipy.sparse import coo_matrix
def get_index_from_click_pattern(click_pattern, location):
index = (location - 1) * 1024
index += int(''.join([st... |
<gh_stars>0
from montepython.likelihood_class import Likelihood
import io_mp
import scipy.integrate
from scipy import interpolate as itp
import os
import numpy as np
import math
# Adapted from <NAME>
class euclid_lensing(Likelihood):
def __init__(self, path, data, command_line):
Likelihood.__init__(sel... |
import os
import torch.nn as nn
import torch
import warnings
import argparse
from Logger import *
import pickle
from Dataset import *
warnings.filterwarnings("ignore")
from Functions import *
from Network import *
import pandas as pd
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu_... |
<reponame>peterewills/NetComp
"""
**********
Resistance
**********
Resistance matrix. Renormalized version, as well as conductance and commute matrices.
"""
import networkx as nx
from numpy import linalg as la
from scipy import linalg as spla
import numpy as np
from scipy.sparse import issparse
from netcomp.linalg.m... |
# -*- coding: utf-8 -*-
"""
Classes and functions used to define 'wind environments'
i.e. the spatial variation of mean and turbulence components of wind speed
@author: RIHY
"""
import scipy
import numpy
import matplotlib.pyplot as plt
from numpy import log as ln
from scipy import spatial
from scipy import interpol... |
from typing import List, Dict, Tuple, Union
from collections import defaultdict
from itertools import groupby
import escnn.nn
from escnn.group import Group, GroupElement
from escnn.group import Representation
from escnn.gspaces import GSpace
from escnn.group import directsum
import numpy as np
from scipy import spa... |
<filename>threeML/minimizer/minimization.py
from __future__ import division
import collections
import math
from builtins import object, range, str, zip
import numpy as np
import pandas as pd
import scipy.optimize
from past.utils import old_div
from threeML.config.config import threeML_config
from threeML.exceptions.... |
<filename>python/redmonster/sandbox/dchi2_optimize.py
# Optimize dchi2 threshold in zfitter for best purity/completeness
import numpy as n
import matplotlib.pyplot as p
p.interactive(True)
from redmonster.sandbox import yanny as y
from astropy.io import fits
from redmonster.datamgr import spec, io
from redmonster.phys... |
# https://deeplearningcourses.com/c/advanced-computer-vision
# https://www.udemy.com/advanced-computer-vision
from __future__ import print_function, division
from builtins import range
# Note: you may need to update your version of future
# sudo pip install -U future
from keras.models import Sequential, Model
from ke... |
<filename>loica/util.py
import pickle
import numpy as np
from scipy.interpolate import interp1d
from scipy.optimize import least_squares
def forward_model_growth(
Dt=0.05,
sim_steps=10,
muval=[0]*100,
od0=0,
nt=100
):
od_list, t_list = [],[]
od = od0
for t in range(nt):
od_list.... |
<filename>fstools/solver.py<gh_stars>0
import torch
import torch
import torch.nn as nn
import torch.nn.functional as F
import scipy.stats as st
import numpy as np
import random
from tqdm import tqdm
def get_loss_mse(alpha, D, X, loss_amp=1, per_elem_batch=False):
"""
Compute MSE loss on batches for weigh... |
<gh_stars>0
from scipy.spatial.distance import cdist
def knowledge_gap(centers1, centers2, metric='seuclidean') -> float:
"""
The knowledge gap is defined as the sum over the distances between centers in <centers1> and the closest center in <centers2>.
Distance is defined by <metric>.
:param centers1... |
import pandas as pd
import numpy as np
import os
from glob import glob
import matplotlib.pyplot as plt
import datetime
from scipy.spatial import distance
from sklearn.impute import KNNImputer
from sklearn.model_selection import train_test_split
from sklearn.metrics.pairwise import paired_distances
from sklearn.preproce... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Sep 19 13:04:59 2020
@author: atkachev
"""
from magnetic import Ap, Bz
import numpy as np
from scipy import integrate
import time
R = np.linspace(0.001, 0.499, 10001)
t1 = time.time()
A1 = np.array([Ap(r,0,0.5,1000) for r in R])
t2 = time.time()
prin... |
"""
============
Rank filters
============
Rank filters are non-linear filters using the local greylevels ordering to
compute the filtered value. This ensemble of filters share a common base: the
local grey-level histogram extraction computed on the neighborhood of a pixel
(defined by a 2D structuring element). If the... |
<filename>behaviorAnalysis/magnification/training/auxiliaries.py
import numpy as np, time, random, csv
import torch, ast, pandas as pd, copy, itertools as it, os, torch.nn as nn
from torchvision import transforms
import torchvision
import scipy.io as sio
from tqdm import tqdm
from PIL import Image
from skimage import i... |
<gh_stars>1-10
from __future__ import print_function
import librosa
from matplotlib import pyplot as plt, ticker as plticker, colors as colors, cm, patches
import librosa.display
from scipy.signal import argrelextrema
import numpy
import pandas as pd
def specshow_localmax(Xdb, grid, percentil):
"""
Máximos lo... |
<reponame>forest-snow/anchor-topic
import scipy.sparse
import numpy
import math
import scipy.stats
def computeQ(word_doc, epsilon=1e-15):
M = scipy.sparse.csc_matrix(word_doc.copy(), dtype=float)
n_words, n_docs = M.shape
# word_probs is sum of probabilities of word occurring in all documents
word_pr... |
from typing import List, Tuple, Dict
import argparse
from scipy.ndimage import fourier_shift, shift
from skimage.feature import register_translation, masked_register_translation
from skimage.transform import rescale
from skimage import io
from shutil import move
from tqdm import tqdm
from parseConfig import parseConfi... |
<reponame>kevin-xuan/Traffic-Benchmark
import pickle
import numpy as np
import os
import scipy.sparse as sp
import torch
from scipy.sparse import linalg
from torch.autograd import Variable
def normal_std(x):
return x.std() * np.sqrt((len(x) - 1.) / (len(x)))
class DataLoaderS(object):
def __init__(self,
... |
import random
import math
import fractions
from ..__init__ import *
from .addition import *
from .subtraction import *
from .multiplication import *
from .division import *
from .binary_complement_1s import *
from .modulo_division import *
from .square_root import *
from .power_rule_differentiation import *
from .sq... |
<reponame>williamgilpin/rk4
# Test out the runge kutta library
# <NAME> 2014
from matplotlib import pyplot
from scipy import *
from numpy import *
from rk4_poincare import *
t = linspace(0, 100.0, 1000)
print ("step size is " + str(t[1]-t[0]))
# Four representative initial conditions for E=1/12 on the Henon-Heiles ... |
<filename>Biological_Study/Plots_For_Enrichment_Jaccard_Mean_Level.py
############################################
# Script to make the plots for the article #
############################################
# Description
'''
This script contains all the fucntions to generate the plots of the enrichment analyses
includ... |
<reponame>TatsuyaHaga/reversereplaymodel_codes<filename>Fig3_Fig4/sample_ISI_speed_symmetric/plot_bias_eachparam.py
#!/usr/bin/env python3
import numpy
import pylab
import scipy.stats
pylab.rcParams["font.size"]=8
pylab.rcParams["legend.fontsize"]=8
#pylab.rcParams["lines.linewidth"]=1
#pylab.rcParams["axes.linewidth... |
import argparse
import multiprocessing
from functools import partial
from io import BytesIO
import lmdb
from PIL import Image
from tqdm import tqdm
import torch
import numpy as np
import pandas as pd
import cv2
import sys
import json
import os
from glob import glob
from utils.CUB_data_utils import square_bbox, pertur... |
import scipy.io as sio
from math import ceil,floor
from statistics import mean
from numpy import square,sqrt,absolute
matfile = sio.loadmat( 'changed_param_2.mat' )
old_mat = sio.loadmat( 'learned_all_param_2.mat' )
#for i in range( 15 ):
#print matfile['p'][[11 ,67 ,225,336,357,444,635,679],0],old_mat['p'][[11 ,67 ,2... |
"""
This file contains various statistical functions used for evaluating the results of a model.
It also helps with the process of performing cross-validation.
Finally, it has multiple functions to display results, as well.
"""
from statistics import mean, stdev
from typing import Union
from aenum import NamedTuple
f... |
<reponame>janvonrickenbach/Chaco_wxPhoenix_py3
"""This example demonstrates creating a contour plot using the chaco
shell subpackage.
"""
# Major library imports
from numpy import linspace, meshgrid, sin
from scipy.special import jn
# Enthought library imports
from chaco.shell import show, title, contour
# Crate som... |
# -*- coding: utf-8 -*-
# SPDX-License-Identifier: Apache-2.0
"""
This module defines the abstract base class for contextual multi-armed bandit algorithms.
"""
import abc
from itertools import chain
from typing import Callable, Dict, List, NoReturn, Optional, Union
import multiprocessing as mp
from joblib import Par... |
from decimal import ROUND_HALF_UP, Decimal
from fractions import Fraction
from django.contrib.gis.db import models
from django.db import connection, transaction
from django.db.models import Max, Sum
from django.utils.translation import pgettext_lazy
from django.utils.translation import ugettext_lazy as _
from enumfiel... |
<gh_stars>0
#!/usr/bin/env python3
##########################################################################################
# For a given spin qudit dimension, this script first generates random measurement axes,
# then takes the best found measurment axes and tries to further optimize them by
# minimizing their ass... |
#!/usr/bin/env python
# coding: utf-8
import sys
sys.path.insert(0, '../py')
from graviti import *
import json
import numpy as np
from skimage.draw import polygon
from skimage import io
from matplotlib import pyplot as plt
import glob
import pandas as pd
import os
from scipy.sparse import coo_matrix
from skimage.me... |
#!/usr/bin/env python
# coding: utf-8
# TODO:
#
#
# R1
# - get the Nyquist plot axis dimensions issue when $k=1$ fixed
# - figure out the failing of .pz with active elements
#
#
# R2
# - make the frequency analysis stuff happen
#
# In[1]:
from skidl.pyspice import *
#can you say cheeky
import PySpice as pspic... |
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 30 01:33:10 2016
@author: Adetola
"""
from __future__ import division
from scipy.stats import nbinom
import numpy.random as random
def corner_spread(home_corners, away_corners, corner_mean, niterations):
random.seed(1234)
game_home_mean = [0] * niterations
g... |
<filename>sdmlib/__init__.py
import scipy.stats as st
import numpy as np
from time import time
class Memory:
def __init__(self, N, M, U, d, T=None, seed=None):
"""
|Parameter|Description|
|:-:|:-:|
|`N`|Length of addresses in bits|
|`M`|Number of hard addresses|
|`U`... |
<reponame>cadurosar/SGC
import numpy as np
import scipy.sparse as sp
import torch
def normalized_adjacency(adj):
adj = sp.coo_matrix(adj)
row_sum = np.array(adj.sum(1))
d_inv_sqrt = np.power(row_sum, -0.5).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
return d_... |
<filename>scripts/gauss_legendre.py
import numpy as np
import sympy as sp
from sympy.core import S, Dummy
from sympy.polys.orthopolys import (legendre_poly, laguerre_poly,
hermite_poly, jacobi_poly)
from sympy.polys.rootoftools import RootOf
def symbolic_gauss_legendre(n):
"""... |
import numpy as np
from scipy.stats import invwishart
"""
Code to simulate data from some simple outcome-covariate relationships.
"""
def make_func(a):
def func(sample_size, D, noise):
r, fx = a()
x = (r[1] - r[0]) * np.random.rand(sample_size, D) + r[0]
y = np.apply_along_axis(lambda x: [... |
import MDSplus as mds
import matplotlib.pyplot as plt
import matplotlib.cm as cm
from matplotlib.widgets import Slider
from scipy.ndimage import median_filter
import sys
import numpy as np
mask_y = np.arange(195, 384, 4)
mask_x = np.arange(3, 512, 8)
def main(argv):
if len(argv) < 3:
sys.stderr.write("Us... |
from .database import Database
from statistics import mean
db = Database.shared()
def main(speed, interval, num_of_tags, posts_threshold=15):
cycle = 1
while True:
cycle_start_time = db.time
tags_rates = {}
for x in db.rates:
# Setting up data points
min_rates_... |
from __future__ import print_function
from __future__ import division
from collections import namedtuple
import logging
import numpy as np
from scipy.optimize import minimize
import open3d as o3
from . import features as ft
from . import cost_functions as cf
from .log import log
class L2DistRegistration(object):
... |
<gh_stars>1-10
import sys
sys.dont_write_bytecode = True
from sympy.core.symbol import Symbol
from sympy.sets.sets import set_function
from sympy import sin, cos, tan, exp, log, sinh, cosh, tanh, atan, diff, sqrt, Piecewise, Max
from autogenu import autogenu
from utils import is_None_dict
from TwoWDRobotState import ... |
import tensorflow as tf
import numpy as np
from scipy import misc
import model
import utils
import graph
import os
import time
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from sklearn.model_selection import train_test_split
from settings import *
data, output_dimension, label = utils.get_dataset(location, picture_dimensi... |
<filename>python/cugraph/tests/test_graph.py
# Copyright (c) 2019, <NAME>.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... |
<gh_stars>1-10
#!/usr/bin/env python3
from timfuz import Benchmark, Ar_di2np, loadc_Ads_b, index_names, A_ds2np, simplify_rows, OrderedSet
import numpy as np
import glob
import math
import json
import sympy
from collections import OrderedDict
from fractions import Fraction
def rm_zero_cols(Ads, verbose=True):
re... |
<gh_stars>1-10
"""
A class for Gaussian process experts with a squared exponential kernel, where each feature is assumed independent
(product). We can optionally have a zero-mean, linear-mean, or constant-mean GP.
Author:
<NAME>
Date:
27/02/2018
"""
from __future__ import division
import GPy
import MixtureOfE... |
import abc
import tensorflow as tf
import numpy as np
from numpy.fft import fftshift, ifftshift
import fractions
import cv2
import os
def tf_compl_exp(phase, dtype=tf.complex64, name='complex_exp'):
"""
Adapted from [Sitzmann et al. 2018]
phase is NOT normalized and should range from -pi to pi
"""
... |
<gh_stars>0
# SUAVE Imports
# Imports
import SUAVE
from SUAVE.Core import Units, Data
from SUAVE.Components.Energy.Networks.Battery_Propeller import Battery_Propeller
from SUAVE.Methods.Geometry.Two_Dimensional.Cross_Section.Airfoil.compute_airfoil_polars import compute_airfoi... |
<filename>scipy/_lib/_numpy_compat.py
"""Functions copypasted from newer versions of numpy.
"""
from __future__ import division, print_function, absolute_import
import warnings
from warnings import WarningMessage
import re
from functools import wraps
import numpy as np
from scipy._lib._version import NumpyVersion
... |
<reponame>SuriyaNitt/DDD
import numpy as np
import os
import warnings
warnings.filterwarnings("ignore")
from sklearn.cross_validation import KFold
from sklearn.metrics import log_loss
import keras
from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten, Merge, Reshape, La... |
<gh_stars>1-10
from __future__ import division
import numpy as np
from scipy import stats, interpolate
class Distribution(object):
"""
draws samples from a one dimensional probability distribution,
by means of inversion of a discrete inverstion of a cumulative density function
the pdf can b... |
import numpy as np
import scipy.optimize as opt
import scipy.spatial.distance as d
import matplotlib.pyplot as plt
'''
Fits data in positions.txt to a Gumbel, Logistic and Boltzmann distribution.
Takes care of edge effects in data (i.e. truncates edge effects
- the edge cases are NOT included in the analysis)
'''
... |
<filename>pyradar/Chapter03/planar_array_example.py
"""
Project: RadarBook
File: planar_array_example.py
Created by: <NAME>
On: 8/1/2018
Created with: PyCharm
"""
import sys
from Chapter03.ui.PlanarArray_ui import Ui_MainWindow
from Libs.antenna.array import planar_uniform
from numpy import linspace, radians, degrees,... |
<reponame>tdalford1/bilby_relative_binning<filename>bilby/core/sampler/ptemcee.py<gh_stars>0
from __future__ import absolute_import, division, print_function
import os
import datetime
import copy
import signal
import sys
import time
import dill
from collections import namedtuple
import numpy as np
import pandas as pd... |
<gh_stars>1-10
from ptsemseg.models.xception39 import xception39
from ptsemseg.models.xception39 import bisenet
from ptsemseg.models.xception39 import bisenet3D
import numpy as np
import torch
from torch.autograd import Variable
# bisenet_model = bisenet(num_classes=1000, pretrained=False)
# bisenet_model.cuda()
# xc... |
<gh_stars>1-10
# coding: utf-8
# In[1]:
#VOTING
import nltk
import random
from nltk.corpus import movie_reviews
from nltk.classify import ClassifierI
from statistics import mode
from nltk.tokenize import word_tokenize
import pickle
class VoteClassifier(ClassifierI):
def __init__(self, *classifiers):
se... |
<filename>bqskit/ir/gates/parameterized/pauli.py
"""This module implements the PauliGate."""
from __future__ import annotations
import os
import numpy as np
import scipy as sp
from bqskit.ir.gates.qubitgate import QubitGate
from bqskit.qis.pauli import PauliMatrices
from bqskit.qis.unitary.differentiable import Diff... |
import scipy.stats as st
import pandas as pd
# related to processing splitseq
def get_bc1_matches():
# from spclass.py - barcodes and their well/primer type identity
bc_file = '/Users/fairliereese/mortazavi_lab/bin/pacbio-splitpipe/barcodes/bc_8nt_v2.csv'
bc_df = pd.read_csv(bc_file, index_col=0, names=['bc'])
b... |
<reponame>odemangeon/bayev<gh_stars>0
import numpy as np
import scipy.linalg
import scipy.stats
import random
import math
def log_sum(log_summands):
a = np.inf
x = log_summands.copy()
while a == np.inf or a == -np.inf or np.isnan(a):
a = x[0] + np.log(1 + np.sum(np.exp(x[1:] - x[0])))
rand... |
import cortex
import glob
from nilearn import surface
from bids import BIDSLayout
import os.path as op
import re
import pandas as pd
import scipy.stats as ss
derivatives = '/data/risk_precision/ds-numrisk/derivatives'
layout_us = BIDSLayout(op.join(derivatives, 'glm_stim1_surf'), validate=False)
layout_s = BIDSLayout... |
# -*- coding: utf-8 -*-
# Authors: <NAME> <<EMAIL>>
# <NAME> <<EMAIL>>
# License: BSD (3-clause)
import numpy as np
from scipy import linalg
from ..io.pick import _pick_data_channels
from ..surface import _normalize_vectors
from ..utils import logger, verbose
from .utils import _get_lims_cola
def _svd_cov... |
from __future__ import division
import numpy as np
import sys
import os
import shutil
import vtk
from vtk.util.numpy_support import vtk_to_numpy
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import matplotlib.animation as animation
import matplotlib.colors as mcolors
import argparse... |
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